Sliding Variable-based Online Adaptive Reinforcement Learning of Uncertain/Disturbed Nonlinear Mechanical Systems

[摘要] In this work, the trajectory tracking control scheme is the framework of optimal control and robust integral of the sign of the error (RISE); sliding mode control technique for an uncertain/disturbed nonlinear robot manipulator without holonomic constraint force is presented. The sliding variable combining with RISE enables to deal with external disturbance and reduced the order of closed systems. The adaptive reinforcement learning technique is proposed by tuning simultaneously the actor–critic network to approximate the control policy and the cost function, respectively. The convergence of weight as well as tracking control problem was determined by theoretical analysis. Finally, the numerical example is investigated to validate the effectiveness of proposed control scheme.

[学科分类] 自动化工程

[关键词] Adaptive dynamic programming (ADP);Robotic systems;Robust integral of the sign of the Error (RISE);Sliding mode control (SMC)